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Millions of developers rely on the Java programming language for web 2.0, big data analytics and scientific computing. It's widely used in large-scale distributed frameworks, like Apache Hadoop, due to its ease of programmability, modularity and multiplatform support.

Duimovich showed an example of GPU acceleration of sorting using standard NVIDIA CUDA libraries that are already available.

The speedups are ranging from 2x to 48x faster. And these benefits are possible in Java JDK 8 by taking advantage of existing CUDA libraries to accelerate the Java libraries for parallel operations.

According to Duimovich, IBM will enable IBM runtimes for server-based GPU accelerators and explore acceleration in ordinary workloads under existing APIs.

This will allow millions of Java developers to accelerate a broad range of applications using GPU accelerators. Plus, the acceleration will fuel a new generation of Java-based enterprise applications.

The use cases for GPU-accelerated Java applications are near endless: from high-performance distributed fraud detection and financial analysis, to high-throughput video and image analytics and modern scientific applications.